Projecting Unemployment Durations: A Factor-Flows Simulation Approach With Application to the COVID-19 Recession* - John Coglianese
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Projecting Unemployment Durations:
A Factor-Flows Simulation Approach With
Application to the COVID-19 Recession*
Gabriel Chodorow-Reich John Coglianese
Harvard Board of Governors
July 2020
Abstract
We propose a three-step factor-flows simulation-based approach to forecast the
duration distribution of unemployment. Step 1: estimate individual transition
hazards across employment, temporary layoff, permanent layoff, quitter, entrant,
and out of the labor force, with each hazard depending on an aggregate compo-
nent as well as an individual’s labor force history. Step 2: relate the aggregate
components to the overall unemployment rate using a factor model. Step 3:
combine the individual duration dependence, factor structure, and an auxiliary
forecast of the unemployment rate to simulate a panel of individual labor force
histories. Applying our approach to the July Blue Chip forecast of the COVID-
19 recession, we project that 1.6 million workers laid off in April 2020 remain
unemployed six months later. Total long-term unemployment rises thereafter
and eventually reaches more 4.5 million individuals unemployed for more than
26 weeks and almost 2 million individuals unemployed for more than 46 weeks.
Long-term unemployment rises even more in a more pessimistic recovery sce-
nario, but remains below the level in the Great Recession due to a high amount
of labor market churn.
* The views expressed herein are those of the authors and not necessarily those of the Board of Governors
of the Federal Reserve System.1 Introduction
Long-term unemployment poses unique challenges to society. Consistent with standard in-
complete insurance models, as jobless spells lengthen, individuals exhaust their savings (Be-
wley, 1980; Ganong and Noel, 2019). The possibility of a deterioration of skills threatens
productivity even after the economy returns to full employment. And the duration struc-
ture of unemployment matters directly to policy, as every recession in the United States
in the past 50 years has included an extension of the number of weeks individuals may
receive government-provided unemployment insurance (UI) benefits. The question of how
many weeks to extend benefits depends in part on how many individuals will reach different
unemployment durations.
Projecting the duration structure of unemployment also poses challenges. Even condi-
tional on a forecast of the overall unemployment rate, the duration structure can vary widely
depending on the magnitude of gross flows across labor force states. For example, ignoring
for the moment transitions in and out of the labor force, an unemployment rate of 9% could
arise from a monthly separation rate into unemployment of 5% and job-finding rate out of
unemployment of 50%, or a separation rate of 2% and job-finding rate of 20%. A job-finding
rate of 50% implies an ergodic distribution in which fewer than 2% of unemployed individu-
als have been unemployed for more than six months, while a job-finding rate of 20% implies
more than one-quarter of unemployed individuals have a duration greater than six months.
Furthermore, the duration distribution also depends on the amount of individual duration
dependence in job finding rates.
In this paper, we introduce a factor-flows simulation-based approach that addresses these
difficulties. The approach has three steps. In the first step, we estimate individual transi-
tion hazards across six labor force states: employment, unemployment on temporary layoff,
permanently laid-off, quitters, entrants, and out of the labor force. The estimated hazards
depend on an individual’s labor force history, consistent with recent work emphasizing the
importance of path dependence in hazard rates (Jarosch, 2015; Yagan, 2019; Kudlyak and
Lange, 2018; Coglianese, 2018; Hall and Kudlyak, 2019), as well as on an aggregate com-
ponent. In the second step, we specify a factor model for the aggregate components of the
hazard rates. In practice, the first principal component explains a large share of the variation
across components and has a correlation of 0.97 with the unemployment rate. We therefore
treat the overall unemployment rate as an observed factor. In the third step, we obtain a
forecast for the overall unemployment rate from an auxiliary source and use the estimated
factor model and individual duration dependence in transition hazard rates to simulate a
1panel of individual labor force histories consistent with the auxiliary forecast of the overall
unemployment rate, the implied aggregate components of the transition hazards, and the
historical pattern of duration dependence.
We apply our approach to the COVID-19 recession. The COVID-19 pandemic impacted
the U.S. labor market in an unprecedented fashion. From a low of 3.5% in February 2020,
the official unemployment rate rose to a post-war high of 14.7% in April.1 The largest prior
two-month increase in the unemployment rate was 1.5 p.p. in 1975. The suddenness of the
unemployment rate increase admits the possibility of a historically rapid recovery. Indeed,
the two month decline from April to June of 3.6 p.p. far surpasses the previous record two
month decline of 1.3 p.p. in 1949. Bolstering this optimism, a historically high share of the
increase in unemployment has occurred in the category of individuals on temporary layoff,
who historically have had high re-employment hazards and may return to work quickly as
lock-down conditions subside. Nonetheless, the sheer scale of the increase in unemployment
raises the possibility of a large cohort of individuals that remain unemployed for a substantial
period of time.
Figure 1 shows the duration distribution in our baseline simulation, separately for those
on layoff (temporary layoff U t and permanent layoff U p ) and other unemployed individuals
(quitters U q and entrants U e ). The average transition hazards and unemployment rate
through June 2020 come from the Current Population Survey (CPS), the source for the
official unemployment rate. After June 2020, the overall unemployment rate follows the
July 2020 Blue Chip Consensus Forecast, which averages the unemployment rate forecasts
from more than 50 leading business economists. Thus, the figure is best interpreted as the
duration distribution implied by the Blue Chip forecast. In this scenario, a historically
rapid recovery following the dramatic increase in unemployment in March and April leaves a
shrinking yet by-historical-standards large stock of individuals without work for more than
six months. Roughly 1.6 million individuals who became unemployed in the initial jump
remain unemployed 6 months later, and half a million remain unemployed in February 2021.
Total long-term unemployment rises thereafter and eventually reaches 4.5 million individuals
1
According to the Bureau of Labor Statistics, survey respondents representing as many
as 7.5 million additional individuals in April reported being temporarily absent from their
jobs and were mis-classified as employed instead of unemployed (https://www.bls.gov/cps/
employment-situation-covid19-faq-may-2020.pdf). Table A.1 reports the number potentially
mis-classified by month and whether the individual was paid as well as the average transi-
tion rate into employed at work. See https://www.brookings.edu/blog/up-front/2020/06/30/
who-are-the-potentially-misclassified-in-the-employment-report/ for further discussion of the
misclassification.
2Figure 1: Projected Unemployment Duration Distribution
15
Simulated share of labor force
12
9
6
3
0
Jan-20 May-20 Sep-20 Jan-21 May-21 Sep-21
t p
U and U : 1-26 weeks 27-46 weeks 47-60 weeks 61+ weeks
q e
U and U : 1-26 weeks 27-46 weeks 47-60 weeks 61+ weeks
Notes: The figure plots the simulated share of the labor force laid off (U t and U p ) and other unemployed
(U q and U e ), by the number of weeks in the past two years the individual has been unemployed. The overall
path of unemployment follows the July Blue Chip Consensus.
unemployed for more than 26 weeks and almost 2 million individuals unemployed for more
than 46 weeks.2
One crucial parameter governing the duration distribution is the re-employment hazard
of workers on temporary layoff. More than 90% of the individuals transitioning from employ-
ment to unemployment in April 2020 reported being on temporary layoff. According to the
CPS classification scheme, these individuals were available to work in the survey reference
week and had either received a recall date from their employer or had been given an indica-
tion that they would be recalled to work within the next six months. Over the period 1994
to 2019, roughly 50% of such individuals returned to employment the following month, more
than double the re-employment hazard for unemployed individuals not on temporary layoff.
The re-employment hazard among both those temporary laid off and others fell in the early
months of the COVID recession, but remained near 40% for those on temporary layoff. At
this rate, fewer than 5% (0.66 ) of the April layoff cohort would remain unemployed in Oc-
tober. However, the re-employment hazard has historically declined especially precipitously
for this group — individuals in their first month of temporary layoff have been re-employed
2
Unemployment durations in this paragraph and throughout the paper refer to the total number of weeks
an individual has been unemployed over the previous two years. This definition differs from the official BLS
definition of consecutive weeks of unemployment, for reasons we discuss below. The numbers are based on
a total civilian, non-institutional population 16 and over of 260 million.
3at a rate of 58%, while those on temporary layoff for two months have a re-employment rate
of 43%. Incorporating this duration dependence, in our simulation 11% of workers laid off
in April remain unemployed in October.
We also simulate two alternative scenarios to demonstrate the flexibility of our approach
and assess the sensitivity of these conclusions. The pessimistic scenario follows the average
of the ten highest Blue Chip unemployment rate forecasts and envisages an unemployment
rate 1.7 p.p. higher than the Consensus in 2020Q4 and throughout 2021, including a second
spike in 2020Q3. The optimistic scenario follows the average of the ten lowest Blue Chip
forecasts and envisages an unemployment rate 1.5 p.p. lower in 2020Q4 and throughout 2021.
Strikingly, despite these very different unemployment rate paths, the amount of long term
unemployment does not differ dramatically across these scenarios. This result reflects the
high degree of labor market churn in the simulations irrespective of the rate of decline of
unemployment.
Our paper complements other research studying the labor market in the early months of
the COVID recession (Bartik et al., 2020; Cajner et al., 2020). Whereas this literature has
examined trends to date, our focus is on future implications, although we necessarily also
cover new ground on the labor market dynamics in the early months of the recession. Similar
to Barrero, Bloom, and Davis (2020), our baseline simulation contains a substantial amount
of labor market reshuffling, despite using a very different methodology. Our methodology also
builds on the aforementioned literature emphasizing duration dependence in labor market
transitions, although we do not take a stand on the source of this dependence (Krueger,
Cramer, and Cho, 2014; Kroft, Lange, and Notowidigdo, 2013; Kroft, Lange, Notowidigdo,
and Katz, 2016; Jarosch and Pilossoph, 2018; Mueller, Spinnewijn, and Topa, 2018; Ahn and
Hamilton, 2020a). We are not aware of previous research empirically associating hazard rates
across labor market states with a factor structure, although the basic search-and-matching
model has this feature.
Section 2 describes our methodology in detail and presents intermediate results including
the underlying transition hazards. Section 3 contains our main results. Section 4 discusses
implications for UI policy.
2 Methodology
Our methodology builds on the unemployment duration simulations in Chodorow-Reich and
Coglianese (2019) by making several important improvements and adjustments tailored to
4the unique aspects of the COVID-19 recession, including allowing for multiple categories of
unemployment and the explicit linking of the hazard rate factor model to unemployment rate
loadings. We therefore provide a comprehensive discussion here, recognizing the foundations
laid in that earlier work.
2.1 Hazard Estimation
The first step of our methodology uses longitudinally-matched monthly data from the Cur-
rent Population Survey (CPS) to estimate hazard rates for transitions across labor market
states. Households drawn into the CPS sample are interviewed up to eight times over a
sixteen month period about the labor force status of their members, with four consecutive
months in sample initially followed by eight months out of sample and then four final consec-
utive months in sample, sometimes referred to as the 4-8-4 rotation group design. We restrict
the sample to respondents who complete all eight interviews and re-weight to account for
non-random attrition in responses.3
Our methodology can accommodate an arbitrarily flexible partition of labor force states,
including by demographic group or occupation. For our application to the COVID recession,
we model six: employed (E), unemployed-temporary layoff (U t ), unemployed-permanent lay-
off (U p ), unemployed-quit (U q ), unemployed-entrant (U e ), and not in the labor force (N ).
Figure 2 illustrates why we split unemployment into these sub-categories. The historical
hazard rates into employment and the incidences of these different categories of unemploy-
ment at the start of the COVID recession differ markedly. Remarkably, the figure reveals the
increase in hiring during the labor market recovery in May and June 2020 to be an entirely
compositional phenomenon; re-employment hazards within both temporary and permanent
3
Figure A.1 shows that respondents who complete all eight interviews have a lower average unemployment
rate than other respondents, and that this bias mostly reflects the lower unemployment rate among any
individual interviewed in consecutive months. The BLS produces research series for gross flows across
employment, unemployment, and out of the labor force that correct for non-random attrition (https:
//www.bls.gov/cps/cps_flows.htm). We re-weight the individuals in our sample to match these corrected
gross flows, applying the same scaling factor to all flows into and out of unemployment states. In this way,
our sample of respondents completing their eighth interview matches overall unemployment dynamics. See
Krueger, Mas, and Niu (2017) and Ahn and Hamilton (2020b) for further discussion of sample attrition
in the CPS. An additional complication arises in the early months of the COVID recession, as response
rates fell dramatically for cohorts entering the CPS sample for the first time, likely reflecting the temporary
discontinuance of in-person interviews for these households. Figure A.2 reports the response rates and
unemployment rates by month-in-sample for 2020. Despite the much lower response rates of cohorts entering
the sample in March-June 2020, the unemployment rate in these cohorts looks quite similar to the overall
unemployment rate, so we make no additional adjustment for these months. Throughout the paper, we use
the CPS longitudinal links created by Drew, Flood, and Warren (2014) and data from Flood et al. (2020)
as well as from the basic monthly CPS files provided by the Census Bureau.
5Figure 2: Re-employment Heterogeneity
Re-employment hazards 2020 unemployment distribution
60.0 90.0
Probability employed next month
80.0
50.0
Percent of unemployed
70.0
40.0 60.0
50.0
30.0
40.0
20.0 30.0
20.0
10.0
10.0
0.0 0.0
2005 2010 2015 2020Mar Apr May Jun Jan Feb Mar Apr May Jun
t p q e t p q e
U U U U U U U U U
Notes: The left panel plots the re-employment probabilities from unemployment overall (U ) and the sub-
categories unemployed-temporary layoff (U t ), unemployed-permanent layoff (U p ), unemployed-quit (U q ),
and unemployed-entrant (U e ) as twelve month moving-averages through February 2020 and the monthly
values thereafter. The right panel plots the distribution of unemployment by status in 2020.
layoff remained below their historical average, but the high share of temporary layoffs and
the historically higher re-employment hazard in this group pulled up the overall hiring rate.
Ignoring this dimension of heterogeneity would cause our simulation to mis-characterize the
amount of labor market churn.
We model each of the thirty-six hazard rates governing the transitions across the six
states s ∈ {E, U t , U p , U q , U e , N } as a function of an individual’s labor market history and
s
aggregate economy wide trends. Let γi,t denote an indicator for individual i in month t being
s0 →s00
in state s and γi,t an indicator for transition from state s0 in period t − 1 to state s00 in
period t. We estimate the OLS regression:
0 00 0 00 0 00
X X
s →s
γi,t = φss,`→s γi,t−`
s
+ δ̄ts →s + i,t . (1)
`∈{2,3,12,13,14,15} s∈{E,U t ,U p ,U q ,U e ,N }
s →s s 0 00
The terms φs,` γi,t−` capture the history-dependence of transition hazards, using all avail-
0 00
able information about an individual’s labor market history in the CPS. The term δ̄ts →s is
a month fixed effect that reflects the state of the aggregate labor market.
We report coefficients from estimating Equation (1) in Figure A.3 of the appendix. For
both temporary and permanent layoff, having been employed in the prior two months pre-
dicts a higher likelihood of re-employment. Differences also exist; for example, conditional
on recent employment history, employment a year ago raises the likelihood of re-employment
6from temporary layoff but not from permanent layoff, and a history of temporary rather than
permanent layoff increases the re-employment hazard for those currently on permanent but
not for those currently on temporary layoff. History also matters to separation probabil-
ities. Individuals with previous spells of unemployment have a higher separation hazard,
and the excess probability concentrates into a return to their previous type (U t or U p ) of
unemployment.
One disadvantage of the CPS rotation structure is that it captures only very recent (past
three months) and distant (one year ago) duration dependence in hazard rates. For most
labor force states, this history encodes most of the relevant heterogeneity. Temporary lay-
offs are an exception, since by definition such spells cannot last more than six months in
expectation. Moreover, as shown in Figure A.4, historically the re-employment hazard for
those on temporary layoff has declined substantially as a spell lengthens from one to two
months. Therefore, for this group we assume the re-employment hazard declines by 24%
(not p.p.) for each additional month an individual remains on temporary layoff, consistent
with the historical relationship over the post 1994 period. Note that if this procedure over-
states the decline in the re-employment hazard, our COVID-19 simulations will overstate
the magnitude of long-term unemployment.
2.2 Factor Model
In the second step, we specify a factor structure for the aggregate components of the tran-
sition hazards:
0 00 0 00 0 00
δ̄ts →s = αs →s + β s →s Ft + νt , (2)
0 00
where the factor loading β s →s encodes the sensitivity of the aggregate component of the
transition rate from s0 to s00 to the common factor Ft . The factor structure has the key
advantage of fixing the ratio of changes in the hazard rates, e.g. when the E → U t hazard
E→N
rises by 1 p.p., the E → N hazard will rise by ββE→U t p.p. In this way, it reduces the
dimensionality of the aggregate component of the simulation from 36 separate transition
hazards to a single factor.
For this dimensionality reduction to work in practice, a single factor must adequately
capture the variation in the transition hazards and this factor must be linked to the auxiliary
unemployment rate forecast. We start by performing principal components analysis on the
12-month moving averages of the 36 aggregate components of the transition hazards (to
7Figure 3: Single Factors for Aggregate Transition Hazard Components
10 8
9 6
8 4
7 2
6 0
5 -2
4 -4
3 -6
2000 2005 2010 2015 2020
Unemployment rate (le9 axis) First principal component (right axis)
Notes: The figure plots the monthly unemployment rate (left-axis) with the first principal component of
the 12-month moving averages of the 36 aggregate components of the transition hazards (right-axis, scale
inverted for comparison).
remove sampling volatility). The first principal component explains 29% of the variance
of these series, more than double the share explained by the second principal component.
Therefore, although even the 12-month moving averages contain substantial idiosyncratic
volatility, a single factor explains the transition hazards well. Next, Figure 3 plots the
first principal component against the overall unemployment rate. The two series co-move
extremely closely, with a correlation coefficient of 0.97. Based on this evidence, we directly
0 00
equate the factor Ft with the overall unemployment rate and estimate the loadings β s →s
from regressions of the aggregate components of the transition hazards on the unemployment
rate. Conveniently, the auxiliary forecast of the unemployment rate then directly determines
the aggregate components of the transition hazards. For our application, we estimate the
factor loadings three times: once using the full sample to capture “typical” dynamics; once
using just the April 2020 observations relative to their historical averages to capture the
dynamics of recent increases; and once using just the May and June 2020 observations to
capture the dynamics of recent declines. These factor loadings are reported in Table A.2.
Before describing the simulation algorithm, several aspects of the factor-flows approach
are worth emphasizing. First, modeling labor market flows across states directly instead of
modeling levels is especially important for the COVID-19 episode; the level of the unem-
ployment rate in April exceeded its previous high by about 35%, but the flow of layoffs—as
8measured by the number of initial UI claims—exceeded its previous high by more than a full
order of magnitude. Second, our approach accounts for both individual-level heterogeneity in
hazard rates and aggregate fluctuations affecting all individuals’ hazard rates. Third, we do
not take a specific stand on the forces that give rise to either individual-level heterogeneity or
aggregate fluctuations. Our approach nests any model in which the individual-level hetero-
geneity depends on the individual’s recent labor market history and aggregate fluctuations
follow a factor model. However, since we estimate the individual heterogeneity on historical
data, the simulations assume these relationships remain stable in the COVID period. This
assumption accords with recent literature viewing separation rates as causally affected by an
unemployment spell (Jarosch, 2015; Hall and Kudlyak, 2019). If instead history dependence
largely reflects selection of who becomes unemployed and the selection mechanism differs in
the COVID recession, our simulations will overstate the amount of history dependence and
hence the amount of long-term unemployment.
2.3 Simulation
0 00 0 00
With estimates of φ`s →s and β s →s in hand, we start the simulation with three burn-in
phases to achieve an unemployment duration distribution that is approximately at steady-
state. In the first phase, we initialize 100,000 individuals with randomly drawn initial states,
using the ergodic distribution of si,t . In the second phase, we simulate an additional 13
months for each individual, drawing si,t from a distribution conditional on si,t−1 , where we use
the national average transition probabilities over 1994-2020. Once we have 14 periods of data,
we construct individual-specific transition probabilities. For each month and individual, we
generate six probabilities for si,t conditional on si,t−1 by applying the estimated duration
0 00
dependence coefficients φ`s →s to the individual’s simulated labor market history and the
0 00
estimated aggregate loadings β s →s to the target unemployment rate. We simulate labor
market histories in this way for 24 months to complete the burn-in.
Starting from the simulated ergodic distribution, we use the actual estimates of the
0 00
aggregate components δ̄ts →s for January 2016 through June 2020 to simulate the initial
conditions and months of the COVID recession. After June 2020, we set the factor so that
the simulated unemployment rate matches the Blue Chip Consensus forecast. The Blue
Chip Economic Indicators is a monthly survey of more than 50 leading business economists.
The survey is conducted over two days at the start of each month, making it more timely
than government forecasts such as those of the Congressional Budget Office (CBO) or the
9Federal Reserve Statement of Economic Projections (SEP).4 Importantly, the July forecasts
incorporate information from the June Employment Situation report from the BLS as well as
rising COVID case counts in parts of the country in the second half of June. The Blue Chip
Consensus is the average across all Blue Chip forecasters and features an unemployment rate
in 2020Q4 of 9.4% in 2021Q4 of 6.9% in 2020Q4.
The Blue Chip Consensus provides the factor Ft for months after June. Given the
unprecedented nature of the COVID labor market, we model July-September 2020 as a
“pandemic recovery”, in which the factor loadings follow their structure in May and June
2020. Thereafter, we assume the factor loadings revert to their average historical values over
our full sample, so that from October 2020 onwards the economy has converted to a “typical”
recession. Throughout, we obtain the duration dependence component of hazard rates at
the individual level from an estimation on the Great Recession period only. Thus, while we
do not explicitly model moral hazard effects of benefit extensions or the large increase in
benefit levels available through the CARES Act, our transition rates implicitly reflect any
impact of moral hazard during the Great Recession.
We also simulate alternative scenarios. The pessimistic scenario follows the average of
the ten highest Blue Chip unemployment rate forecasts and envisages an unemployment rate
1.7 p.p. higher than the Consensus in 2020Q4 and throughout 2021, including a second spike
in 2020Q3.5 The optimistic scenario follows the average of the ten lowest Blue Chip forecasts
and envisages an unemployment rate 1.5 p.p. lower in 2020Q4 and throughout 2021.
3 Results
In this section we discuss several noteworthy aspects of our application to the COVID-19
recession.
4
The most recent forecast from the CBO was released on July 2 but finalized on June 26 and likely quasi-
finalized well before that date (https://www.cbo.gov/publication/56442). Notably, the CBO forecast of
the unemployment rate in 2020Q2 of 13.8% substantially exceeds the actual 2020Q2 average unemployment
rate of 13.0%. The most recent SEP was released on June 10 and has a median forecast close to the Blue
Chip Consensus.
5
For the second spike, we slightly deviate from our baseline approach and assume that the factor loadings
follow the same structure in July–September 2020 as they had in April 2020. Afterwards, we assume that
the factor loadings revert to their average historical values from October 2020 onwards, the same as in our
baseline simulation.
103.1 Prevalence of Long-term Unemployment
For each unemployed individual, we define months unemployed as the total amount of time
that individual has spent in unemployment in the previous two years. This definition differs
from the official BLS definition of consecutive months of unemployment. It has the ad-
vantage of mitigating the problem of persons spuriously reporting that they moved between
unemployment and out of the labor force, which would also contaminate our simulation since
it uses actual labor force histories as input data (see Ahn and Hamilton (2020b) for further
discussion). It also better accords with policy questions such as unemployment insurance
(UI) exhaustion, which depends on total weeks of benefit receipt even when interrupted by
an employment spell.
In the baseline simulation, the number of individuals unemployed for longer than 26
weeks peaks in February 2021 at 4.5 million. Very long-term unemployment peaks about a
year later in early 2022 with nearly 2 million individuals unemployed for more than 46 weeks
and 1.1 million unemployed for longer than 60 weeks. Although high, these magnitudes
fall below the corresponding peak levels in the Great Recession, despite a higher overall
unemployment rate peak in the COVID epsiode. This difference reflects the high degree of
churn and relatively rapid labor market recovery in our simulation, as we describe next.
3.2 Churn
Figure 4a shows actual and projected gross hires and separations in each month. These flows
remain elevated relative to their pre-recession levels. This pattern reflects two complemen-
tary forces. First, the high share of unemployed on temporary recall predicts relatively high
re-employment hazards, at least for the next few months. Matching the Blue Chip forecast
of the unemployment rate then requires that separations also remain high. Notably, initial
claims for unemployment insurance at the end of June remained at double their peak in the
Great Recession. Despite not targeting UI claims data, our simulation matches the implied
separation rate closely. Second, as an increasing number of individuals experience unemploy-
ment, the model perceives their attachment to their employer to decline. This deterioration
reflects the historical tendency for individuals with recent non-employment to separate from
their employer more frequently than those with long spells of employment. In the specific
circumstance of the COVID recession, it could reflect workers in socially interactive jobs
most vulnerable to health-related fluctuations in demand. The high amount of churn re-
duces the prevalence of long-term unemployment at a given level of the unemployment rate,
11Figure 4: Churn
(a) Projected Hires and Separations (b) Unemployment by Layoff Cohort
15
25
Simulated unemployment rate
20
10
15
Millions
10
5
5
0
Jan-2020 May-2020 Sep-2020 Jan-2021 May-2021 Sep-2021
0
Jan-2020 May-2020 Sep-2020 Jan-2021 May-2021 Sep-2021
Cohort temp. laid off in: April 2020 May 2020 June 2020
Hires Separations All other unemployment
Notes: The left figure plots the number of hires from non-employment and separations to non-employment
in our baseline simulation. The right figure plots the portion of unemployment attributable to different
layoff cohorts. Cohorts are defined based on E → U t flows in each month, but all subsequent months of
unemployment are counted as attributable to that cohort, including months of U p , U q , and U e .
even in our expansive definition that allows for temporary periods of re-employment.
Figure 4b provides another perspective on the amount of churn by plotting the share of
unemployed in each month that first entered unemployment in the April 2020 spike. In the
actual data, about 11 million individuals transitioned from employment to unemployment in
April 2020, with more than 90% of these individuals on temporary layoff. Among the April
2020 temporary layoff cohort, 40% were re-employed in May and 58% were re-employed in
June. As a result, those entering temporary layoff in April 2020 account for less than one-
third of total unemployment in June. Our simulation tracks these flows closely. On the other
hand, those remaining unemployed face lower re-employment hazards, either because they
have transitioned to permanent layoff or out of the labor force or because of the negative
duration dependence among the temporary unemployed, and even those re-employed have
higher separation hazards due to the history dependence. These characteristics explain why
the April 2020 cohort remains a non-trivial share of total unemployment even in 2021.
We can also ask what the projected churn implies for reallocation dynamics. Overall, two-
thirds of the April 2020 temporary layoff cohort eventually transition back to employment
directly from temporary unemployment. Associating these individuals with recalls yields
a share of the April 2020 cohort returning to their previous employer of two-thirds, in the
range of the forecast made in Barrero, Bloom, and Davis (2020) despite our using a very
12Figure 5: Alternative Scenarios: Projected Duration Distributions
Pessimistic Scenario Optimistic Scenario
15 15
12 12
Simulated share of labor force
Simulated share of labor force
9 9
6 6
3 3
0 0
Jan-20 May-20 Sep-20 Jan-21 May-21 Sep-21 Jan-20 May-20 Sep-20 Jan-21 May-21 Sep-21
t p
U and U : 1-26 weeks 27-46 weeks 47-60 weeks 61+ weeks
q e
U and U : 1-26 weeks 27-46 weeks 47-60 weeks 61+ weeks
Notes: The figure plots the simulated share of the labor force laid off (U t and U p ) and other unemployed
(U q and U p), by the number of weeks in the past two years the individual has been unemployed. The overall
path of unemployment follows the July Blue Chip average of the 10 highest unemployment rate forecasts
(left panel) or ten lowest unemployment rate forecasts (right panel).
different methodology.6
3.3 Alternative Scenarios
The Blue Chip Consensus forecast and our simulation involve a historically rapid labor
market recovery. The left panel of Figure 5 shows the duration distribution in a more
pessimistic scenario in which the unemployment rate follows the average of the 10 highest
Blue Chip forecasts and exceeds 11% in 2020Q4 before falling to 8.6% in 2021Q4. Perhaps
surprisingly, despite the slower labor market recovery, long-term unemployment does not
rise that much relative to the baseline scenario, with the number unemployed more than 6
months peaking at 5.1 million.
The right panel of Figure 5 shows the duration distribution in an optimistic scenario in
which the unemployment rate follows the average of the 10 lowest Blue Chip forecasts and
6
Fujita and Moscarini (2017) present evidence that ex post recall typically exceeds ex ante expected recall.
We suspect that if anything the opposite will be true in the COVID recession.
13falls to 8% in 2020Q4 and 5.4% in 2021Q4. This scenario contains less long-term unemploy-
ment, with the number unemployed for more than 6 months peaking around 3.9 million, but
again does not differ dramatically from the baseline or even pessimistic scenario. The expla-
nation lies in the assumed churn, which does not differ too much across all three scenarios.
As a result, while the pessimistic scenario contains a lower hiring rate and higher separation
rate than the baseline, and the optimistic scenario a higher hiring rate and lower separation
rate, these differences have a limited impact on the amount of long-term unemployment.
By contrast, if the labor market becomes more sclerotic than in our simulations, long-term
unemployment could rise much more.
4 Implications
We conclude by highlighting the implications for unemployment insurance (UI). In normal
periods, individuals may claim UI benefits from their state insurance programs, typically
for up to 26 weeks. The joint federal-state Extended Benefits (EB) program provides up
to an additional 20 weeks of benefits in states with high unemployment. Finally, in every
recession since 1950, the federal government has enacted temporary recipiency tiers that
allow individuals to receive benefits after exhausting their regular state program benefits.
The CARES Act provides an extension of 13 weeks known as Pandemic Emergency Unem-
ployment Compensation (PEUC), currently scheduled to expire in December 2020. In the
Great Recession, federal emergency tiers peaked at 53 weeks, allowing individuals to claim
benefits for a total of up to 99 weeks, generating fierce political debate (Chodorow-Reich,
Coglianese, and Karabarbounis, 2019).
As policy-makers debate whether or how much to extend federal benefits, our results
provide some guidance on the number of individuals affected by different extension lengths.
For example, in our baseline scenario a sizable number of potentially eligible (i.e. laid off)
individuals — more than 3.6 million at the peak — will have unemployment durations beyond
the 26 weeks provided by regular state benefits. However, most of these individuals return
to employment before 46 weeks, suggesting that they might be covered under the existing
EB architecture if that structure were fully utilized.7 A larger number of individuals remain
unemployed for very long durations in the pessimistic scenario, providing a possible rationale
for economic-based triggers to govern additional extensions.
7
Under existing law states may opt out of EB triggers, and the majority do so, presumably because states
must pay for 50% of EB.
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16Projecting Unemployment Durations:
A Factor-Flows Simulation Approach With Application to the
COVID-19 Recession
Online Appendix
Gabriel Chodorow-Reich John Coglianese
17Figure A.1: Longitudinally-matched Unemployment
14.0
12.0
Unemployment rate
10.0
8.0
6.0
4.0
2.0
0.0
Apr-19 Jun-19 Aug-19 Oct-19 Dec-19 Feb-20 Apr-20
All Group 8 and all interviews Matched
Notes: The figure plots the unemployment rate in the full CPS sample (solid black line), respondents in
their eight interview who have completed all previous interviews (dashed blue line), and any respondent
completing an interview in the second consecutive month (dotted red line).
Figure A.2: Rotation Group Response Rates and Unemployment in 2020
100.0 14.0
14.0
90.0 12.0
Unemployment rate
12.0 10.0
Response rate
80.0
Unemployment rate
10.0
8.0
70.0
8.0
6.0
6.0
60.0
4.0
4.0
50.0 2.0
2.0
40.0
0.0 0.0
Jan
Jan Feb MarFeb Apr May Jun
Mar Jan
Apr Feb Mar May Apr May Jun
Jun
Rotation group: 1 2 3 4 5 6 7 8
Notes: The left panel plots the response rate by rotation group and month in 2020. The right panel plots
the unweighted, not seasonally adjusted unemployment rate by rotation group and month.
18Table A.1: Persons Employed And Absent from Job for Other Reasons
Number of persons (millions) Re-employment hazard (%)
All Unpaid Paid All Unpaid Paid
Mar-19 0.65 0.53 0.12 62.2 57.4 84.3
Apr-19 0.55 0.44 0.11 59.1 59.0 59.3
May-19 0.48 0.39 0.09 43.7 42.5 53.0
Jun-19 0.92 0.52 0.40
Jan-20 0.77 0.69 0.08 55.7 56.7 41.6
Feb-20 0.60 0.55 0.05 44.1 40.7 76.2
Mar-20 2.13 1.54 0.58 33.8 26.8 51.7
Apr-20 8.07 6.15 1.92 34.7 32.1 42.9
May-20 5.43 4.39 1.04 41.3 40.5 44.9
Jun-20 2.83 2.08 0.76
The table reports the number of persons employed and absent from their job for “other” reasons (left panel),
and the transition rate of these individuals into employed-at work (right panel), separately by whether the
absence is paid or not.
19Figure A.3: Duration Dependence Coefficients
U*→E regression U5→E regression
100
Coefficient (p.p. effect on hazard rate)
0
-100
E→U* regression E→U5 regression
100
0
-100
t-2 t-3 t-12 t-13 t-14 t-15 t-2 t-3 t-12 t-13 t-14 t-15
E U* U5 U6 U7
Notes: The figure plots the coefficients estimated from Equation (1) for the U t → E (top left panel), U p → E
(top right panel), E → U t (bottom left panel), and E → U p (bottom right panel) flows. The coefficients
on N are omitted to avoid multicollinearity. The sample includes CPS respondents who complete all eight
interviews over the period of May 1995 to June 2020.
20Figure A.4: Re-employment Hazard of Temporary Layoffs by Duration
80.0
Probability employed next month
60.0
40.0
20.0
0.0
1994 1998 2002 2006 2010 2014 2018
On temporary layoff for: one month two months
Notes: The figure plots the twelve-month moving average of the re-employment hazard for individuals on
temporary layoff for one and two months.
21Table A.2: Estimated Factor Loading Coefficients
Flow type Sensitivity to unemp. rate
Full Sample April 2020 May/June 2020
t
E→U 0.10 (0.07) 0.07 (--) 0.72 (--)
E→Up 0.05 (0.01) 0.00 (--) 0.02 (--)
E→Uq -0.01 (0.00) 0.00 (--) 0.00 (--)
E→Ue 0.00 (0.00) -0.01 (--) -0.01 (--)
E→N 0.01 (0.02) 0.06 (--) 0.19 (--)
Ut→E -0.85 (0.35) -2.08 (--) -2.87 (--)
Ut→Up 0.63 (0.18) 0.10 (--) -0.04 (--)
Ut→Uq -0.07 (0.03) -0.06 (--) -0.08 (--)
Ut→Ue 0.06 (0.08) 0.56 (--) -0.12 (--)
Ut→N -0.19 (0.19) -0.35 (--) 0.17 (--)
Up→E -1.45 (0.16) -0.31 (--) -1.19 (--)
Up→Ut 0.07 (0.08) -0.01 (--) 0.79 (--)
Up→Uq -0.04 (0.02) -0.02 (--) -0.03 (--)
Up→Ue 0.00 (0.01) -0.01 (--) -0.01 (--)
Up→N -0.15 (0.28) 0.81 (--) 1.64 (--)
Uq→E -0.81 (0.44) 1.73 (--) 0.35 (--)
Uq→Ut -0.02 (0.05) -0.04 (--) -0.05 (--)
Uq→Up -0.02 (0.09) -0.13 (--) -0.07 (--)
Uq→Ue 0.01 (0.04) -0.06 (--) -0.03 (--)
Uq→N 0.19 (0.34) 0.78 (--) 1.30 (--)
Ue→E -1.29 (0.15) -0.67 (--) -0.88 (--)
Ue→Ut 0.04 (0.04) -0.03 (--) -0.02 (--)
Ue→Up 0.02 (0.01) 0.00 (--) -0.01 (--)
Ue→Uq 0.00 (0.00) 0.00 (--) 0.00 (--)
Ue→N -0.06 (0.21) -0.38 (--) 2.24 (--)
N→E -0.12 (0.02) -0.07 (--) -0.22 (--)
N→Ut 0.00 (0.00) 0.00 (--) 0.15 (--)
N→Up 0.07 (0.01) 0.03 (--) 0.00 (--)
N→Uq -0.01 (0.00) -0.01 (--) -0.01 (--)
N→Ue 0.03 (0.01) -0.04 (--) -0.05 (--)
0 00
Notes: Each coefficient β s →s comes from a separate regression of Equation (2). The “Full Sample”
estimates use monthly data from May 1995 through December 2019, and heteroskedasticity-robust standard
errors are reported in parentheses. The “April 2020” estimates use only two data points in each regression:
the historical average over 2015–19, and April 2020. The “May/June 2020” estimates similarly use the
historical average, and the average of May and June 2020. As a result, standard errors are not defined for
the latter sets of regressions. Only 30 out of the 36 transitions
are shown for simplicity, since the omitted flows
t p q e
are linearly dependent on the flows shown, e.g. β E→E = − β E→U + β E→U + β E→U + β E→U + β E→N .
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